The purpose of this grant is to examine the relationship between the number of hospital beds in a geographical area and the area's hospitalization rate. Traditional analyses of the relationship between hospital supply and utilization have determined an area's supply of hospital beds by allocating beds to areas based on the area's utilization pattern. However, this method introduces a bias. Since supply is determined by utilization, a correlation between supply and hospitalization rates is induced by the analytical methods even if no such correlation really exists. Our approach avoids allocating beds to areas based on observed utilization. Rather, we will use a Bayesian hierarchical model to decompose observed utilization into two components: "true" utilization patterns and effects induced by hospitals. By allocating beds to areas based on "true" utilization patterns, we will be able to examine the relationship between supply and utilization in a way that avoids the bias toward overestimating the correlation that results from traditional methods of analysis. [unreadable] [unreadable] We will use two different data-sets, both of which we already have, for the analysis. The first data set includes both inpatient and outpatient data for Medicare recipients in Massachusetts in 1997. The second data set consists of hospital discharge abstract data for all Massachusetts residents from 1997 to 2000. When using the Medicare data, we will be able to separately estimate three effects: "true" utilization, a hospital-induced effect and an area-specific effect that reflects the amount of diagnosed disease in the area. When using the other data set, we will not be able to separately estimate an area-specific effect. However, we will be able to perform the analysis using data from all payors and multiple years. We will examine the sensitivity of the estimated relationship between bed supply and utilization for different degrees of homogeneity in the way in which areas are defined. [unreadable] [unreadable] By measuring the strength of supply-induced demand in a way that avoids over-estimation bias, we will contribute insight information to the policy debate about the value of constraining hospital capacity in certain geographical areas in order to affect demand. [unreadable] [unreadable] [unreadable]